Simulation of 3D tumor cell growth using nonlinear finite element method

Large Growth Deformations of Thin Tissue Using Solid-Shells

Simulating large scale expansion of thin structures, such as in growing leaves, is challenging. Solid-shells have a number of potential advantages over conventional thin-shell methods, but have thus far only been investigated for small plastic deformation cases. In response, we present a new general-purpose FEM growth framework for handling a wide range of challenging growth scenarios using the solid-shell element. Solid-shells are a middle-ground between traditional volume and thin-shell elements where volumetric characteristics are retained while being treatable as a 2D manifold much like thin-shells. These elements are adaptable to accommodate the many techniques that are required for simulating large and intricate plastic deformations, including morphogen diffusion, plastic embedding, strain-aware adaptive remeshing, and collision handling. We demonstrate the capabilities of growing solid-shells in reproducing buckling, rippling, curling, and collision deformations, relevant towards animating growing leaves, flowers, and other thin structures. Solid-shells are compared side-by-side with thin-shells to examine their bending behavior and runtime performance. The experiments demonstrate that solid-shells are a viable alternative to thin-shells for simulating large and intricate growth deformations.

Multi-scale modeling of hippo signaling identifies homeostatic control by YAP-LATS negative feedback

The Hippo signaling primarily includes LATS1/2 and YAP1. Recent work has demonstrated a novel negative feedback between YAP1 and LATS1/2. However, how YAP-LATS negative feedback regulates cancer progression remains elusive. We constructed a multi-scale model which integrates angiogenesis, spatiotemporal variation of microenvironmental factors and phenotypic switch of tumor cells. Our simulation replicated the findings that YAP overexpression markedly attenuated cell proliferation owing to elevated negative feedback strength. After disruption of YAP-LATS negative feedback loop, however, YAP overexpression would promote cell proliferation. Consistently, LATS overexpression inhibited cell growth and lowered the proliferation potential. We also employed a putative LATS agonist and identified its dose-dependent tumor suppressive effects. Furthermore, targeted delivery could more effectively inhibit tumor growth. Our model has reconciled experimental findings and implied that reconstruction of functional and/or hyperactivated YAP-LATS negative feedback might be a promising strategy to homeostatic maintenance and tumor suppression.

Simulation of 3D centimeter-scale continuum tumor growth at sub-millimeter resolution via distributed computing

Simulation of cm-scale tumor growth has generally been constrained by the computational cost to numerically solve the associated equations, with models limited to representing mm-scale or smaller tumors. While the work has proven useful to the study of small tumors and micro-metastases, a biologically-relevant simulation of cm-scale masses as would be typically detected and treated in patients has remained an elusive goal. This study presents a distributed computing (parallelized) implementation of a mixture model of tumor growth to simulate 3D cm-scale vascularized tissue at sub-mm resolution. The numerical solving scheme utilizes a two-stage parallelization framework. The solution is written for GPU computation using the CUDA framework, which handles all Multigrid-related computations. Message Passing Interface (MPI) handles distribution of information across multiple processes, freeing the program from RAM and the processing limitations found on single systems. On each system, Nvidia's CUDA library allows for fast processing of model data using GPU-bound computing on fewer systems. The results show that a combined MPI-CUDA implementation enables the continuum modeling of cm-scale tumors at reasonable computational cost. Further work to calibrate model parameters to particular tumor conditions could enable simulation of patient-specific tumors for clinical application.